Signal strength profiling

ABSTRACT

A system for supporting electronic positioning in apparatuses. Apparatuses may receive various wireless signals over time. The strength of the received wireless signals may be determined by the apparatuses, and based on this determination an occurrence for each received wireless signal may be accumulated in a histogram in the apparatus. Information based on the histogram may then be transmitted to a mapping database. The mapping database may then utilize this information to, for example, create signal-based maps for use in apparatus positioning.

This is a divisional application of co-pending patent application Ser.No. 13/821,583 filed on May 21, 2013 (hereby incorporated by reference),which is the U.S. National Stage of International application numberPCT/IB2010/054102 filed on Sep. 10, 2010 which was published in Englishon Mar. 15, 2012 under International Publication number WO 2012/032376.

BACKGROUND

1. Field of Invention

The present invention relates to electronic positioning, and inparticular, to collecting and normalizing electronic signal informationfor use in signal-based mapping.

2. Background

The integration of wireless communication functionality into bothexisting and emerging applications continues to expand. Strong demandhas spurred developers to not only create more powerful communicationdevices, but also to introduce other helpful applications that rely uponwireless communication for support. In this manner, wirelesscommunication has moved beyond the mere conveyance of voice data, andhas evolved to make possible various applications for personalproductivity, business, entertainment, etc.

At least one popular communication-based application that has emerged iselectronic positioning. Electronic positioning may provide currentlocation information for apparatuses in terms of coordinates, inrelation to visual presentation (e.g., map), etc. However, the means forobtaining information upon which a position is determined may vary. Forexample, apparatuses may include Global Positioning System (GPS)receivers for obtaining the electronic positioning information fromsatellites. Long-range wireless communication systems (e.g., cellular)may also provide positioning information through methods such ascell-based triangulation, while short-range wireless systems may be ableto provide information through determination of proximity to wirelessaccess points, etc.

These wireless communication systems may serve as adequate sources ofinformation for supporting positioning systems in the out-of-doorsbecause, in addition to position information being readily available forproviding quick position resolution, maps for most populated regions areavailable and frequently updated. However, these same advantages do notexist when attempting to implement electronic positioning inside of astructure (e.g., buildings). Accurate map information (or any mapinformation) is often not available, and the electronic positioningsignals relied upon for outside positioning may not be as dependablebased on the prevalent sources of interference that are found inside ofstructures. As a result, electronic position resolution within astructure may be very slow, if even available, and may be lack theaccuracy required to be effective.

SUMMARY

Example embodiments of the present invention may be directed to amethod, computer program product, apparatus and system for supportingelectronic positioning in apparatuses. Apparatuses may receive variouswireless signals over time. The strength of the received wirelesssignals may be determined by the apparatuses, and based on thisdetermination an occurrence for each received wireless signal may beaccumulated in a histogram in the apparatus. Information based on thehistogram may then be transmitted to a mapping database. The mappingdatabase may then utilize this information to, for example, createsignal-based maps for use in apparatus positioning.

In at least one example implementation, apparatuses may receive wirelesssignals while a user is traversing a particular geographic area. Thesewireless signals may be received from other wireless communicationapparatuses, like access points, residing in or near the particulargeographic area. Upon receiving these signals, apparatuses may determinea source for each signal (e.g., a particular access point) based oninformation contained in each received signal and may measure thestrength of each received signal. The signal strength information may beaccumulated in a histogram within the apparatus. Signal sourceidentification and/or information derived from the histogram may be usedin the apparatus for positioning purposes. It may also be possible forthis information to be transmitted to the mapping database for use incoverage area map formulation, or in some instances, for requestingremote positioning assistance. Information derived from the histogrammay comprise one or more of maximum signal strength, minimum signalstrength, median signal strength, mode signal strength, and mean signalstrength and at least one of quartile and tertile signal strength rangesbased on signal strength probability mass, or other information that maybe usable by the mapping database for scaling, filtering or normalizingsignal strength information. In some instances, filtering based onparameters such as received signal strength may occur prior to anyinformation being sent to the mapping database.

Source identification and information derived from histograms in variousapparatuses may be received at a mapping database. In instances wherethis information is for use in coverage area map formulation, filteringbased on parameters such as a threshold signal strength value may beperformed prior to use. In instances where the mapping database receivesa request for positioning assistance from an apparatus, the mappingdatabase may reply to the request by indicating a type of coverage areamap to utilize for positioning in the requesting apparatus.Alternatively, responses may comprise threshold signal strengthinformation for use selecting received signals for positioning, theidentification of certain signal sources, etc. Threshold signal strengthmay, for example, specify a minimum signal strength for use inpositioning so that only signals having a signal strength over thethreshold value are used to determine apparatus position.

The foregoing summary includes example embodiments of the presentinvention that are not intended to be limiting. The above embodimentsare used merely to explain selected aspects or steps that may beutilized in implementations of the present invention. However, it isreadily apparent that one or more aspects, or steps, pertaining to anexample embodiment can be combined with one or more aspects, or steps,of other embodiments to create new embodiments still within the scope ofthe present invention. Therefore, persons of ordinary skill in the artwould appreciate that various embodiments of the present invention mayincorporate aspects from other embodiments, or may be implemented incombination with other embodiments.

DESCRIPTION OF DRAWINGS

The invention will be further understood from the following descriptionof various example embodiments, taken in conjunction with appendeddrawings, in which:

FIG. 1 discloses example apparatuses, communication configuration andnetwork architecture usable in implementing at least one embodiment ofthe present invention.

FIG. 2 discloses additional detail with respect to example communicationinterfaces usable with at least one embodiment of the present invention.

FIG. 3 discloses additional detail with respect to example closeproximity and short range wireless resources usable with at least oneembodiment of the present invention.

FIG. 4 discloses an example operational environment and the challengespresented therein in accordance with at least one embodiment of thepresent invention.

FIG. 5A discloses an example electronic positioning scenario based onwireless signals received by an apparatus in accordance with at leastone embodiment of the present invention.

FIG. 5B discloses an example of the relationship between positioningerror and signal sources in accordance with at least one embodiment ofthe present invention.

FIG. 6A discloses an example signal strength disposition in accordancewith at least one embodiment of the present invention.

FIG. 6B discloses an example electronic positioning scenario based on areduced number of wireless signals received by an apparatus inaccordance with at least one embodiment of the present invention.

FIG. 7 discloses an example histogram based on received signal strengthin accordance with at least one embodiment of the present invention.

FIG. 8A discloses a flowchart for an example histogram creation andtransmission process in accordance with at least one embodiment of thepresent invention.

FIG. 8B discloses a flowchart for an example mapping databaseinteraction process in accordance with at least one embodiment of thepresent invention.

DESCRIPTION OF EXAMPLE EMBODIMENTS

While the invention has been described below in terms of a multitude ofexample embodiments, various changes can be made therein withoutdeparting from the spirit and scope of the invention, as described inthe appended claims.

I. Example System with which Embodiments of the Present Invention may beImplemented

An example of a system that is usable for implementing variousembodiments of the present invention is disclosed in FIG. 1. The systemcomprises elements that may be included in, or omitted from,configurations depending, for example, on the requirements of aparticular application, and therefore, is not intended to limit presentinvention in any manner.

Computing device 100 may correspond to various processing-enabledapparatuses including, but not limited to, micro personal computers(UMPC), netbooks, laptop computers, desktop computers, engineeringworkstations, personal digital assistants (PDA), computerized watches,wired or wireless terminals/nodes/etc., mobile handsets, set-top boxes,personal video recorders (PVR), automatic teller machines (ATM), gameconsoles, or the like. Elements that represent basic example componentscomprising functional elements in computing device 100 are disclosed at102-108. Processor 102 may include one or more devices configured toexecute instructions. In at least one scenario, the execution of programcode (e.g., groups of computer-executable instructions stored in amemory) by processor 102 may cause computing device 100 to performprocesses including, for example, method steps that may result in data,events or other output activities. Processor 102 may be a dedicated(e.g., monolithic) microprocessor device, or may be part of a compositedevice such as an ASIC, gate array, multi-chip module (MCM), etc.

Processor 102 may be electronically coupled to other functionalcomponents in computing device 100 via a wired or wireless bus. Forexample, processor 102 may access memory 104 in order to obtain storedinformation (e.g., program code, data, etc.) for use during processing.Memory 104 may generally include removable or imbedded memories thatoperate in a static or dynamic mode. Further, memory 104 may includeread only memories (ROM), random access memories (RAM), and rewritablememories such as Flash, EPROM, etc. Examples of removable storage mediabased on magnetic, electronic and/or optical technologies are shown at100 I/O in FIG. 1, and may serve, for instance, as a data input/outputmeans. Code may include any interpreted or compiled computer languageincluding computer-executable instructions. The code and/or data may beused to create software modules such as operating systems, communicationutilities, user interfaces, more specialized program modules, etc.

One or more interfaces 106 may also be coupled to various components incomputing device 100. These interfaces may allow for inter-apparatuscommunication (e.g., a software or protocol interface),apparatus-to-apparatus communication (e.g., a wired or wirelesscommunication interface) and even apparatus to user communication (e.g.,a user interface). These interfaces allow components within computingdevice 100, other apparatuses and users to interact with computingdevice 100. Further, interfaces 106 may communicate machine-readabledata, such as electronic, magnetic or optical signals embodied on acomputer readable medium, or may translate the actions of users intoactivity that may be understood by computing device 100 (e.g., typing ona keyboard, speaking into the receiver of a cellular handset, touchingan icon on a touch screen device, etc.) Interfaces 106 may further allowprocessor 102 and/or memory 104 to interact with other modules 108. Forexample, other modules 108 may comprise one or more componentssupporting more specialized functionality provided by computing device100.

Computing device 100 may interact with other apparatuses via variousnetworks as further shown in FIG. 1. For example, hub 110 may providewired and/or wireless support to devices such as computer 114 and server116. Hub 110 may be further coupled to router 112 that allows devices onthe local area network (LAN) to interact with devices on a wide areanetwork (WAN, such as Internet 120). In such a scenario, another router130 may transmit information to, and receive information from, router112 so that devices on each LAN may communicate. Further, all of thecomponents depicted in this example configuration are not necessary forimplementation of the present invention. For example, in the LANserviced by router 130 no additional hub is needed since thisfunctionality may be supported by the router.

Further, interaction with remote devices may be supported by variousproviders of short and long range wireless communication 140. Theseproviders may use, for example, long range terrestrial-based cellularsystems and satellite communication, and/or short-range wireless accesspoints in order to provide a wireless connection to Internet 120. Forexample, personal digital assistant (PDA) 142 and cellular handset 144may communicate with computing device 100 via an Internet connectionprovided by a provider of wireless communication 140. Similarfunctionality may be included in devices, such as laptop computer 146,in the form of hardware and/or software resources configured to allowshort and/or long range wireless communication. Further, any or all ofthe disclosed apparatuses may engage in direct interaction, such as inthe short-range wireless interaction shown between laptop 146 andwireless-enabled apparatus 148. Example wireless enabled apparatuses 148may range from more complex standalone wireless-enabled devices toperipheral devices for supporting functionality in apparatuses likelaptop 146.

Further detail regarding example interface component 106, shown withrespect to computing device 100 in FIG. 1, is now discussed with respectto FIG. 2. Initially, interfaces such as disclosed at 106 are notlimited to use only with computing device 100, which is utilized hereinonly for the sake of explanation. As a result, interface features may beimplemented in any of the apparatuses that are disclosed in FIG. 1(e.g., 142, 144, etc.) As previously set forth, interfaces 106 mayinclude interfaces both for communicating data to computing apparatus100 (e.g., as identified at 200) and other types of interfaces 220including, for example, user interface 222. A representative group ofapparatus-level interfaces is disclosed at 200. For example, multiradiocontroller 202 may manage the interoperation of long range wirelessinterfaces 204 (e.g., cellular voice and data networks), short-rangewireless interfaces 206 (e.g., Bluetooth and WLAN networks),close-proximity wireless interfaces 208 (e.g., for interactions whereelectronic, magnetic, electromagnetic and optical information scannersinterpret machine-readable data), wired interfaces 210 (e.g., Ethernet),etc. The example interfaces shown in FIG. 2 have been presented only forthe sake of explanation herein, and thus, are not intended to limit thevarious embodiments of the present invention to utilization of anyparticular interface. Embodiments of the present invention may alsoutilize interfaces that are not specifically identified in FIG. 2.

Multiradio controller 202 may manage the operation of some or all ofinterfaces 204-210. For example, multiradio controller 202 may preventinterfaces that could interfere with each other from operating at thesame time by allocating specific time periods during which eachinterface is permitted to operate. Further, multiradio controller 202may be able to process environmental information, such as sensedinterference in the operational environment, to select an interface thatwill be more resilient to the interference. These multiradio controlscenarios are not meant to encompass an exhaustive list of possiblecontrol functionality, but are merely given as examples of howmultiradio controller 202 may interact with interfaces 204-210 in FIG.2.

The example communication interface configuration 106 disclosed in FIG.2 may, in accordance with at least one embodiment of the presentinvention, further comprise example close-proximity wireless interfaces208 such as set forth in FIG. 3. Resources for visual sensing maycomprise at least a camera or similar sensor device capable of recordingmoving and/or still image data, light/dark data, color data, etc. Otherexamples of close-proximity sensing interfaces that may be incorporatedin apparatuses may include, but are not limited to,transmission/reception interfaces for Near Field Communication (NFC),radio frequency (RF) transceivers for communicating data such as radiofrequency identification (RFID) information, magnetic sensors formagnetic ink character recognition (MICR), magnetic field detection,etc., and infrared (IR) transmitters/receivers for communicating IRinformation over short distances.

Moreover, example short-range wireless interface 206 may comprisehardware and/or software resources for supporting various forms ofshort-range wireless communication. Examples of wireless communicationthat may be supported by interface 206 may include, but are not limitedto, wireless local-area networking (WLAN), Bluetooth (BT) communication,Bluetooth Low Energy (BTLE) communication, wireless Universal Serial Bus(WUSB) communication, Ultra-wideband (UWB), etc. These forms ofcommunication may, in various applications, support wireless interactionbetween two or more handheld wireless communication devices, betweenhandheld wireless communication devices and stationary access points(AP), to peripheral interface devices, etc.

II. Example Operational Environment

Assisted global positioning (A-GPS) and other electronic positioningsolutions based on wireless communication may perform acceptably and mayprovide extensive coverage outdoors where the signal quality and numberof satellites/base stations are typically very good. This performancemay be bolstered by accurate maps featuring terrain features, roads,traffic conditions and other related information have been mappedexhaustively and are constantly maintained from satellite images, aerialphotography, feedback from user communities, etc. Together, theavailable positioning solutions and the feature-rich maps may provideexcellent user experiences (e.g., such as in instances including vehicleand pedestrian navigation use).

The situation becomes totally different when the navigation is broughtindoors. Known positioning technologies have very limited capabilitiesindoors, and thus, usually fail. There are many reasons for thesefailures. Initially, existing positioning/mapping solutions may beexpensive and difficult to implement. Map information does not exist formany public/private structures, and the provision of this informationrequires extensive modeling visualization and/or mapping that iscurrently only provided by private companies. Further, existingsolutions may provide unstable and/or unpredictable performance, whichmay occur to do external positioning signals being unavailable orunreliable and indoor signals lacking sufficient position resolution.

The various embodiments of the present invention may provide a means forfaster and more accurate position determination in scenarios wheretraditional positioning techniques may be unavailable or inadequate. Anexample of a problematic situation is providing electronic positioningwithin a structure such as a building. While positioning within abuilding will be utilized for the sake of explanation herein, thevarious embodiments of the present invention are not limited only to usein this specific application. Almost any situation where traditionalpositioning techniques do not provide adequate performance (e.g., speed,resolution, etc.) may experience improvement through the followingexample embodiments and/or implementations of the present invention.

Partial floor plan 400 disclosed in FIG. 4 will help to explain variouschallenges to traditional electronic positioning that may be experiencedwithin a structure (e.g., building). Information received fromsatellites 402 and long-range transmission 404 (e.g., cellular) may beeffective when outside where these signals may be efficiently received.However, some structures may totally block, significantly reflect, orjust render such long-range signals unreliable so as to createunacceptable results for positioning purposes as shown in FIG. 4. User406 (e.g., a user that is equipped with, or is carrying, at least oneapparatus such as the example apparatuses previously described herein)may then have to rely on wireless electronic communication providedwithin a building in order to electronically determine position. Forexample, wireless access points (AP) 408, 410, 412 and 414 may provideelectronic wireless communication as shown in floor plan 400. Inadditional to simply providing data communication (e.g., access to theLAN or WAN resources such as the Internet), these access points may alsoprovide positioning information. Various methods for determiningposition may be employed, each with differing amounts of accuracy. Forexample, connection 416 to a particular AP (e.g., AP 408) indicates thatuser 406 is within communication range of that AP. However, theresolution provided by such an estimation is extremely inexact,especially within the smaller confines of a building.

Further, signals from various access points may be utilized for variousposition finding algorithms. For example location triangulation based onconnection to more than one AP or direct-of-arrival (DoA) estimation inorder to determine the relative direction from a receiver towards theemitter of a signal may be employed. However, the various signals 416emitted by AP 408-414 may experience substantial interference/reflectionpoints 418 within a building or structure. For example, walls containingmetallic conduits, hallways containing various corners and otherobjects, elevator shafts and other high power equipment may cause signalinterference or reflection 418. Interference/reflection points 418 mayresult in AP signals 416 being delayed significantly, or not beingreceived at all. Further, these signals may be received from directionsthat do not indicate the actual direction from which the signal wasoriginally sent, and thus, may cause delays or inaccuracy when employingthese traditional position finding techniques.

III. Example Positioning Based on Signal Strength Fingerprints

As set forth above, fingerprints may comprise various types ofinformation sensed by apparatuses at a particular location. Whilevarious types of information may be sensed (e.g., visual, electronic,magnetic, etc.), the following disclosure will focus on how wirelesscommunication signal information may be collected/processed forexplaining the various embodiments of the present invention. However,the various embodiments of the present invention are not limited tobeing implemented only with wireless communication signals, and maytherefore be applied to any similar scenario in which information sensedby an apparatus is collected and/or processed for use in determiningapparatus position.

Radio-based positioning/mapping technologies may utilize communicationnode coverage area information to estimate apparatus position. Suchtechnologies may be implemented using long-range wireless systems (e.g.,cellular network base stations) or short-range wireless transmissiondevices like WLAN access points (APs). Position may then be estimatedbased on models that describe system configuration and/or geographicfeatures for a coverage area. Coverage area models may come in variousforms. For example, in cellular networks position estimation may bebased on model information including base station location, antennaazimuth, beam width and transmission range. Moreover, geographicalinformation may also be utilized to model radio propagation based onpropagation models.

In instances where positioning is based on short-range wireless signals,multiple samples (e.g., fingerprints) may be collected using apparatusesthat also include some sort of independent positioning capability, suchas a GPS receiver. The signal characteristics and position informationmay be used in formulating radio signal-based maps, or radiomaps, foruse in providing position estimation to other apparatuses that do notcontain dedicated positioning resources. Example fingerprints mayinclude fingerprint location and a list of communication node identities(e.g., Cell IDs or WLAN AP MAC addresses). Moreover, observed signalstrength values and various other pieces of information may be includedin fingerprints in order to link the strength of sensed signals to theactual location of the apparatus. The fingerprints may then be refinedin various ways to formulate a coverage area model/radiomap. One optionmay be to simply model the maximum coverage area. Alternatively, acoverage area may be modeled statistically. For example, coverage areasmay be considered as Gaussian distributions or mixtures. In suchinstances coverage area models actually model a distribution offingerprint collectors (users) within the true node coverage area.

In example position determination processes apparatuses may receivesignals from various communication nodes and may use information withinthe received signals to determine the identities of the signal sources.Apparatus position may then be estimated based on the intersection ofthe node coverage areas. Alternatively, coverage areas may be modeled asstatistical objects having mean and covariance. The coverage areas oftransmitting apparatuses (e.g., APs) may be considered as, for example,Gaussian measurements. Modeling the area in this manner may, forexample, allow for deducing a mean or maximum a posteriori (MAP)estimate of the location of the apparatus.

However, it is possible that considering all available information whenan apparatus is determining position may lead to inaccurate estimates.FIG. 5A discloses an example scenario in this regard which builds uponthe example set forth in FIG. 4. An apparatus possessed by user 406 mayreceive signals from many access points that are within range of thecurrent position of user 406. For example, in FIG. 5A the apparatus mayreceive transmissions from APs 502-514. APs 502-514 reside at differentdistances from user 406, and thus, the strength of each received signalis different as signified by the thickness of the lines describing thesignals in FIG. 5A. For example, the signals received from AP 502 aredarkest since AP 502 is situated closest to user 406, while the signalsreceived from APs 510-514 are represented very lightly since these APsare situated further away. It may seem logical that the more informationthat is available, the more accurate the estimation. However, FIG. 5Bwill demonstrate that the large expanse of area 500 may actuallycontribute in inaccuracy in position determination.

FIG. 5B discloses an error chart 520 in which the number of WLAN APsused in positioning has been mapped versus mean error in meters.Positioning in FIG. 5B is based on having Gaussian coverage area modelsand an apparatus assigned to a location that is the weighted average ofthe coverage area center points. Contrary to what could be expected, theerror was seen to increase when the number of used WLAN APs increases.The increased error may be due in part to uncertainty that is introducedwhen the number of detected APs increases in that a larger share of theobserved APs are actually situated further away from the apparatus. Anincreased number of distant APs may influence the accuracy of thelocation estimate (e.g., draw the estimate towards the more distantAPs).

IV. Increasing Accuracy by Limiting the Information used in Positioning

These results demonstrate a need to limit the fingerprints used informing the coverage area models as well as to somehow select a subsetof the observed APs for positioning. Limiting coverage area based onreceived signal strength indication (RSSI) values during the radiomaplearning process may provide increased positioning accuracy through themore selective utilization of information. FIG. 6A discloses equi-signalstrength boundaries corresponding to mean, mode and minimum (sensitivitythreshold) RSSI values for an example access point in a structure 600.Interestingly, the greater the signal strength value used as theboundary value, the better the coverage area mimics a symmetric shapeincluding ellipses. Ellipses are commonly used for modeling coverageareas, especially in cases where the coverage area is considered instatistical manner.

Now referring to FIG. 6B, example elliptical RSSI measurements for eachof APs 502-508 is now disclosed. APs 510-514 have been omitted in orderto provide some clarity in FIG. 6B, but these APs may exhibit similarstrength boundaries. For example, ellipses 602A-C may model the mean,mode and minimum RSSI observed by user 406 for AP 502. Likewise,ellipses 604A-C may correspond to similar values observed by user 406for AP 504 and ellipses 608A-C may correspond to similar values for AP508. The coverage area may be modeled as the covariance of themulti-normal distribution that has an elliptical geometrical shape.Therefore, it may be advantageous to formulate coverage area modelsconsidering only the fingerprints that have RSSI values that exceed athreshold value. In FIG. 6B, some of these observed signal RSSI valuesmay be strong enough (e.g., over a threshold RSSI value) to be used forreporting, positioning, etc. Usable observed RSSI values are indicatedby heavier lines in FIG. 6B. For example, any of the observed signals602A-C may be usable by user 406, depending on the particular coveragearea model employed, due to the close proximity of AP 502. On the otherhand, the signal strength of AP 506 may be too weak for use in reportingto the mapping database, for positioning, etc. Some of the observed RSSIvalues (e.g., 604A-B and 608A-B) may be usable from APs 504-508. Animprovement in positioning accuracy may be achieved when using coveragearea models that have been formed considering only fingerprints in whichRSSI values for sensed APs exceed some threshold value, such as mean,median and mode average RSSI values.

When coverage area models are based on observed RSSI values that aregreater than mean (“>mean RSSI”), only fingerprints (FPs) with RSSIvalues exceeding mean value are used in coverage area modeling.Similarly, when performing location estimation in view of the thesecoverage area models, only APs that are observed to have RSSIs exceedingthe mean value will be used in positioning. Both of these requirementsexclude APs that are further away from the terminal so that only theclosest APs are taken into account. It can be observed that, in general,operating using such threshold value requirements may halve positioningerror. However, an issue to consider when using RSSI values is that WLANchipset vendors may utilize different RSSI definitions that result inRSSI scales and offsets that vary between chipsets. For example, thesame physical signal strength range (e.g., [−30, −20] dB ref 1 mW) maybe expressed as an RSSI range [0, 10] in one chipset and an RSSI range[−40,−20] in another. These differences present a challenge whenformulating coverage area models based on RSSI since fingerprints may becontributed by a wide range of apparatuses utilizing a wide range ofchipsets, and any radiomaps resulting from the contribution offingerprint information may be used to position a various apparatuseshaving the same chipset issues. Moreover, apparatuses that contain thesame chipsets may also exhibit different situational performance.Parameters such as apparatus condition (e.g., was the apparatus recentlycalibrated, repaired, dropped or damaged in some manner), configuration(e.g., contained in a case or carrier), current apparatus power, loadingor processing levels, etc. may also affect sensitivity. The use ofhistograms to normalize sensing operation eliminates the need to knownthis information.

In accordance with at least one embodiment of the present invention, theaccuracy of positioning may be enhanced by collecting histogram data onobserved RSSI values both in apparatuses that are collectingfingerprints and in apparatuses performing positioning based onestablished coverage area models. When the number of observed RSSIvalues increases, the collection of histogram data may provide data thatconverges towards real distributions of RSSI values in a givenapparatus. When reporting fingerprints, which in some instances maycomprise providing location information along with signal sourceidentification information (e.g., WLAN ID) corresponding to receivedsignals and RSSI values for received signals, apparatuses may alsoreport histogram characteristic values. Example characteristic valuesmay include maximum received signal strength, minimum received signalstrength, mean average received signal strength, median average receivedsignal strength, mode average received signal strength, and quartileand/or tertile received signal strength ranges based on signal strengthprobability mass. Coverage area models may be based on fingerprintshaving RSSI values greater than the given characteristics, and thus,even if “median” RSSI varies from apparatus to apparatus, the fact thatapparatuses also report the RSSI value corresponding to the signalcharacteristic “median” deduced from histograms with large sample spaceallows for coherent “median” coverage area models to formulated. Thehistogram characteristics, such as median, used to limit the set offingerprints used for modeling may be stored along with the coveragearea model, which may allow for normalizing the concept ofcharacteristic “median” across various apparatus types. This allowsapparatuses to utilize only those APs for which the observed RSSIexceeds the characteristic RSSI value based on which the models havebeen formed.

Accurate coverage area models based on fingerprints exhibiting minimumsignal strength (e.g., >mean) cannot simply be based upon setting anRSSI limit to some globally applicable numerical value (e.g., −60).Although an actual observed mean signal strength may be “−30 dB ref 1mW” across all apparatuses, this value might correspond to an RSSImeasurement of “−50” in some apparatuses and “−34” in other apparatusesbased on manufacturer/model/etc. Therefore, it becomes beneficial foreach apparatus that is collecting fingerprint data to collect observedRSSI measurements over time in order to establish statisticalcharacteristics particular to the apparatus. Having this informationallows for the normalization of RSSI measurements between fingerprintsreceived from various apparatuses. This information may be collected ina histogram that summarizes observed RSSI values over time. Suchhistograms may be easy to implement regardless of the abilities of theapparatus. For example, apparatuses may scan for wireless signals andcounters may be incremented for each observed RSSI value. These countersmay be incremented based on the number of signal received detected ateach RSSI value. Signals received from different APs may have the sameobserved RSSI value, and so the counter corresponding to the observedRSSI value may be increased based on each occurrence of the receivedsignal. For example, if a scan detected wireless signals from five APshaving RSSI values of {−59, −38, −90, −48, −38}, the histogram counters(or bins) corresponding to −59, −90 and −48 may be increased by one andthe bin corresponding to −38 may be increased by two. In this mannerapparatuses may continually accumulate histogram information, possiblyeven in the background to avoid apparatus operation disruption. Thestorage requirements for maintaining histograms in apparatuses are verysmall. For example, in a configuration with 128 RSSI bins in the range[0,−127], and for each bin there is a 32-bit (4 byte) counter toaccommodate more than 4 billion samples, the storage requirement for thehistogram would be a negligible 512 bytes. Using a 32-bit counter meansthat even if the counter for a given bin would be incremented by 10every second, the counter would roll-over only after 14 years, whichprovides substantial overhead.

It may be assumed that over time the histogram information will convergeto represent a real distribution of RSSI values observed by theterminal. It may then be straightforward to extract key information fromthe histogram including maximum RSSI value observed by the terminal,minimum RSSI value observed by the terminal (e.g., in order to establisha sensitivity threshold), median RSSI observed by the terminal, modeRSSI observed by the terminal, which may be deemed the most likely RSSIto occur, mean RSSI observed by the terminal and quartile and/or tertileRSSI ranges based on the probability mass in the RSSI histogram. Theaccumulation of histogram information allows each apparatus to be ableto determine the numerical RSSI value corresponding to the “mean RSSI”based on its own observed values, which allows for the normalization ofvalues like “mean RSSI” across various apparatuses.

FIG. 7 discloses an example histogram at 700. Histogram 700 shows thatthe distribution of RSSI values may be skewed towards low signalstrengths. This trend may be expected because, in general, at any givenlocation there are more signal sources that are distant from theterminal than there are close to the terminal. In accordance with atleast one embodiment of the present invention, RSSI characteristics mayused in at least three ways. Initially, as a part of area model creationfingerprints containing signal sources (e.g., WLAN AP IDs), theircorresponding RSSI values and potentially also the location of thefingerprint was recorded may be provided to the mapping database for usein formulating an area model. Apparatuses may also report histogramcharacteristics (e.g., numerical RSSI values corresponding to min, max,mean, median, mode, quartiles, tertiles, etc.) As previously discussedabove, the histogram characteristic information may be utilized by themapping database, for example, in excluding fingerprints having RSSIslower than some threshold values (e.g., mean, mode, median, etc.) sothat coherent, coverage area models, such as “mean RSSI” coverage areamodels, can be generated from the fingerprints originating from avariety of end user apparatuses.

When formulating coverage area models for use in determining apparatusposition, a mapping database may actually formulate one or more areamodels. Coverage area models may include information limited to aspecific value type (such as “mean”) that has been used in generatingthe coverage area model. In that case only fingerprints that exceed“mean” value have been used in coverage area estimation. Alternatively,the mapping database may maintain several coverage area models for agiven AP, each of which models may be based on a limiting value of adifferent type. In this second example application of histogramcharacteristic information, apparatuses may submit fingerprints andcorresponding histogram information, and the mapping database maydetermine the particular coverage area model in which the fingerprintshould be incorporated based on the histogram characteristicinformation. In such instances the various coverage area models maycorrespond to different characteristic information types such as acoverage area map being formulated for use with mean signal information,a coverage area map being formulated for use with median signalinformation, a coverage area map being formulated for use with modesignal information, etc. It also follows that fingerprints used informulating mean, median or mode coverage area maps would also be usablein formulating a coverage area map corresponding to at least “minimumsignal strength.” In a positioning system comprising individual maps, itmay be important for an apparatus to first determine a type of coveragearea to use.

In this regard, a third example application using histogramcharacteristic information pertains to position determining operations.In a first example mode of operation apparatuses may simply utilizeinformation derived from their internal histogram to determine whatsignals should be used for positioning. For example, assume that inpositioning the apparatus has a “median” coverage area models for APsproximate to the apparatus. Now, based on the information derived fromthe histogram the apparatus knows the numerical RSSI corresponding to“median RSSI” (e.g., −60) for that particular apparatus. The apparatusmay then select only those APs for positioning whose observed RSSIexceeds −60. Apparatuses may also request positioning assistance from amapping database in order to determine types of coverage area modelsthat are available for positioning including, for example, whether theavailable coverage area models are based on >mean RSSI values, >minimumRSSI values, etc. In at least one example implementation, apparatusesreport back all observed APs and possibly histogram characteristics to amapping database (e.g., server). Based on the coverage area models thatare available for reported APs, the mapping database may then decide thecoverage area models (mean, mode, median, etc.) that should be used bythe requesting apparatuses. Alternatively, apparatuses may report backonly those APs for which their observed RSSI exceeds a threshold valuesuch as “median RSSI” based on the terminal's histogram characteristics.Reporting apparatuses may also request certain parameters such asconsideration of “median coverage areas.” In accordance with at leastone embodiment of the present invention, when providing positioningassistance the mapping database may also instruct apparatuses to reportonly APs for which an observed RSSI exceeds a “median RSSI” threshold.Apparatus position may then be determined based on only the subset ofAPs for which the RSSI exceeds the RSSI threshold value.

In an example of system operation, apparatuses in an area may collectfingerprints. Fingerprints may contain the location where thefingerprint was recorded, identification of the signal sources (e.g.,WLAN AP IDs) heard at the location and the corresponding RSSI values.When apparatuses report fingerprints to a mapping database, thesefingerprints may be reported along with apparatus-specific histogramcharacteristics such as RSSI values corresponding to minimum, maximum,mode, mean, median, quartiles (ranges), tertiles (ranges) observed bythe particular reporting apparatus.

The mapping database may receive fingerprints from a multitude ofapparatuses. In instances where the mapping database is configured toconsider “mode” coverage areas, for a given observed signal source(e.g., WLAN AP) the database may only consider those fingerprints thathave RSSI values above the “mode” threshold level. Although actualnumerical RSSI values that may correspond to “mode” may vary fromapparatus to apparatus, the terminal-specific information received witheach report allows for filtering, normalizing, etc. of the data in thereceived fingerprints.

Apparatuses that desire to perform positioning may do so based on theirown internal information, or via assistance from the mapping database.Apparatuses that rely on their own internal information may deriveinformation from an internal histogram (e.g., mean, mode or minimumRSSI) that allows them to filter out only access points for use inpositioning that have RSSI levels above a threshold value. When remoteresources (e.g., a mapping database) are relied upon to providepositioning assistance, apparatuses may provide characteristicinformation to the mapping database. Other information may also beprovided instead of, or along with, the characteristic information suchas a list of observed APs. The mapping database may then sendinformation back to the apparatus to assist in positioning. For example,the mapping database may indicate an appropriate type of coverage areamodel (e.g., mean, mode, median, etc.) for the apparatus to use inpositioning, or may even select APs to utilize for positioning.Alternatively, radiomaps (e.g., a collection of coverage area models)may be transmitted to apparatuses for use in positioning. Whenapparatuses commence position determination, an initial check may bemade as to whether the radiomap received from the mapping databaseincludes a coverage area map calibrated based on “mode” averageinformation. If a mode map is present, apparatuses may scan for signalsand obtain RSSI values for observed signals corresponding to signalsources (e.g., WLAN APs). Apparatuses may then verify, based on internalhistogram, which RSSI values are above the threshold in the mode areamodel. Apparatuses may only consider APs whose RSSI value exceed thismode signal strength threshold. The APs that exceed this threshold maythen be used in determining apparatus position.

A flowchart of an example histogram creation and management process, inaccordance with at least one embodiment of the present invention, isdisclosed in FIG. 8A. In step 800 an apparatus may receive wirelesssignals. The reception of signals in step 800 may optionally includeidentification of a source for the received signals based on information(e.g., WLAN AP ID) contained in the signals. Received signal strengthmay then be measured in step 802, for example, in terms of RSSI. Theprocess may then move to step 804 where the signal strength informationis added to a histogram in the apparatus. A determination may then bemade in step 806 as to whether information from the histogram should beprovided to a mapping database. This decision may be based on variousfactors including, for example, time parameters (e.g., interval orduration from the last provision of information to the mappingdatabase), an amount of data collected, the need for apparatuses tointeract with the mapping database (e.g., to request positioningassistance), etc. If the information should not be provided to themapping database at this time, then the process may return to step 800in preparation for the next scan for/receipt of wireless signals in theapparatus (e.g., to continue information collection).

Alternatively, if it is determined in step 806 that the information inthe apparatus should be reported to the mapping database, the processmay proceed to step 808 where a further determination may be made as towhether the information observed by the apparatus should further befiltered before being provided to the database. In accordance withvarious embodiments of the present invention, filtering may take placeeither at the apparatus level or at the database level. If no filteringis deemed necessary, then in step 810 information based on the histogrammay be provided to the mapping database. This information may include,for example, observed RSSI for signals received in the apparatus,identified sources corresponding to each signal, other fingerprintinformation (e.g., the position of the apparatus when the signals werereceived) and characteristic information derived from the histogram suchas maximum RSSI, minimum RSSI, mean RSSI, median RSSI, mode RSSI, and atleast one of quartile and/or tertile RSSI ranges based on signalstrength probability mass. The process may then terminate in step 812and may return to step 800 in preparation for receiving new wirelesssignals. Alternatively, if in step 808 a determination is made thatfiltering is necessary prior to transmitting the information to themapping database, then filtering may proceed in step 814. Filtering mayinclude, for example, determining which APs have observed RSSImeasurements above a certain threshold value (e.g., mean, median, mode),and sending only the information associated with these APs to themapping database in step 810. For example, the presence of a certain APmay be observed in several different fingerprints. In such circumstancesit may happen that a subset of the fingerprints get reported (e.g.,fingerprints in which the RSSI observed for the AP exceeds the specifiedthreshold). The process may then terminate in step 812 and return tostep 800 as previously described.

Another process directed to activities performed by a mapping database,in accordance with at least one embodiment of the present invention, isdisclosed in FIG. 8B. In step 820 the mapping database may receiveinformation from other apparatuses. Interaction with the mappingdatabase may serve at least two different functions. Other apparatusesmay provide information to the mapping database for use in formulatingcoverage area maps, or other apparatuses may request positioningassistance (e.g., help with determining the current position of theapparatus). In step 822 a determination is made to determine the subjectfunctionality. If positioning assistance is being requested in step 822,then in step 824 information may be provided from the requestingapparatus (e.g., identified APs proximate to the requesting apparatus,information derived from the histogram of the requesting apparatus,etc.) to which the mapping database may respond. For example, themapping database may provide a coverage area map type (e.g., mode, mean,median) for use in selecting appropriate APs for use in positioning.Alternatively, the mapping database may provide the identification ofparticular APs whose signals the apparatus should use when estimatingposition. In this latter instance, the mapping database may perform thefiltering/selection processes for the apparatus and just respond withthe actual APs to use in positioning. The process may then be completein step 826 and may return to step 820 in preparation for furtherinformation provided by apparatuses.

If in step 822 it is determined that information is being received inthe mapping database for use in formulating coverage area maps, then instep 828 a further determination may be made as to whether theinformation should first be filtered before inclusion in coverage areamap formulation. If no filtering is determined to be required in step828, the process may proceed directly to coverage area map formulationin step 830. The process may then terminate in step 826 and return tostep 820 in preparation for the next receipt of information. Iffiltering is determined to be required in step 828, then in step 832consideration of whether the received fingerprint information should beincluded in coverage area map formulation may first be conducted beforemoving to step 830. As previously discussed, the mapping database mayperform filtering to determine whether received fingerprint informationhas a corresponding observed RSSI value that is above a threshold value(e.g., mode, mean, median RSSI value), the threshold value being derivedfrom the histogram information received along with the fingerprints. Ifthe RSSI value corresponding to the fingerprint exceeds the thresholdvalue, then the fingerprint may be acceptable for use in coverage areamap formulation. Further to filtering step 832, a determination may bemade in step 834 as to whether the fingerprint should be used incoverage area map formulation. If the fingerprint should be used, theprocess may return to step 830 for coverage area map formulation.Otherwise, the process may then terminate in step 826 and may return tostep 820 in preparation for further information provided by apparatuses.

While various example configurations of the present invention have beendisclosed above, the present invention is not strictly limited to theprevious embodiments.

For example, the present invention may include, in accordance with atleast one example embodiment, an apparatus comprising means forreceiving wireless signals in an apparatus, means for determining asignal strength for each received wireless signal, and means foraccumulating an occurrence for each received wireless signal based onthe determined signal strength in a histogram maintained within theapparatus.

At least one other example embodiment of the present invention mayinclude, in accordance with at least one example embodiment, anapparatus comprising means for receiving from apparatuses at leastsignal source identification and information derived from signalstrength histograms at a mapping database, and means for creatingcoverage area maps in the mapping database corresponding to theidentified signal sources utilizing at least the received identities andinformation.

At least one other example embodiment of the present invention mayinclude electronic signals that cause apparatuses to receive wirelesssignals, determine a signal strength for each received wireless signal,and accumulate an occurrence for each received wireless signal based onthe determined signal strength in a histogram maintained within theapparatus.

At least one other example embodiment of the present invention mayinclude electronic signals that cause apparatuses to receive fromapparatuses at least signal source identification and informationderived from signal strength histograms at a mapping database, andcreate coverage area maps in the mapping database corresponding to theidentified signal sources utilizing at least the received identities andinformation.

Accordingly, it will be apparent to persons skilled in the relevant artthat various changes in forma and detail can be made therein withoutdeparting from the spirit and scope of the invention. The breadth andscope of the present invention should not be limited by any of theabove-described example embodiments, but should be defined only inaccordance with the following claims and their equivalents.

What is claimed:
 1. A method, comprising: receiving, at a mappingdatabase, from apparatuses at least signal source identification andinformation derived from signal strength histograms, each histogramcomprising a range of signal strength values divided into a series ofconsecutive, non-overlapping signal strength bins, wherein eachhistogram records a total number of occurrences of signal strengthvalues of signals received from multiple signal sources observed by anapparatus of said apparatuses at a respective observing location thatfall into each signal strength bin; creating, by the mapping database,coverage area maps in the mapping database corresponding to theidentified signal sources utilizing at least the received signal sourceidentities and information derived from said signal strength histograms,wherein the coverage area maps are created using only receivedinformation with a signal strength that is above a threshold value, andwherein the threshold value is at least one of a median signal strength,mode signal strength or mean signal strength; receiving from arequesting apparatus a request for positioning assistance at the mappingdatabase; and transmitting at least one of a coverage area map type ofthe coverage area maps to utilize in positioning or identities of signalsources to which the coverage area maps correspond to utilize inpositioning to the requesting apparatus, wherein the map type is atleast one of a median type, a mode type or a mean type.
 2. The method ofclaim 1, further comprising: transmitting the identity identities ofparticular signal sources to which the coverage area maps correspond toutilize in positioning to the requesting apparatus.
 3. A computerprogram product comprising computer executable program code recorded ona non-transitory computer readable storage medium, the computerexecutable program code configured to, when executed by at least oneprocessor, cause an apparatus to: receive, at a mapping database, fromapparatuses at least signal source identification and informationderived from signal strength histograms, each histogram comprising arange of signal strength values divided into a series of consecutive,non-overlapping signal strength bins, wherein each histogram records atotal number of occurrences of signal strength values of signalsreceived from multiple signal sources observed by an apparatus of saidapparatuses at a respective observing location that fall into eachsignal strength bin; create coverage area maps in the mapping databasecorresponding to the identified signal sources utilizing at least thereceived signal source identities and information derived from saidsignal strength histograms, wherein the coverage area maps are createdusing only received information having a corresponding signal strengththat is above a threshold value, and wherein the threshold value is atleast one of a median signal strength, mode signal strength or meansignal strength; receive from a requesting apparatus a request forpositioning assistance at the mapping database; and transmit at leastone of a coverage area map type of the coverage area maps to utilize inpositioning or identities of signal sources to which the coverage areamaps correspond to utilize in positioning to the requesting apparatus,wherein the map type is at least one of a median type, a mode type or amean type.
 4. The computer program product of claim 3, wherein thecomputer program code is further configured to, in cooperation with theat least one processor, cause the apparatus to transmit identities ofparticular signal sources to which the coverage area maps correspond toutilize in positioning to the requesting apparatus.
 5. An apparatus,comprising: at least one processor; and at least one memory includingexecutable instructions, the at least one memory including computerprogram code configured to, in cooperation with the at least oneprocessor, cause the apparatus to perform at least the following:receive, at a mapping database, from apparatuses at least signal sourceidentification and information derived from signal strength histograms,each histogram comprising a range of signal strength values divided intoa series of consecutive, non-overlapping signal strength bins, whereineach histogram records a total number of occurrences of signal strengthvalues of signals received from multiple signal sources observed by anapparatus of said apparatuses at a respective observing location thatfall into each signal strength bin; create coverage area maps in themapping database corresponding to the identified signal sourcesutilizing at least the received signal source identities and informationderived from said signal strength histograms, wherein the coverage areamaps are created using only received information having a correspondingsignal strength that is above a threshold value, and wherein thethreshold value is at least one of a median signal strength, mode signalstrength or mean signal strength; receive from a requesting apparatus arequest for positioning assistance at the mapping database; and transmitat least one of a coverage area map type of the coverage area maps toutilize in positioning or identities of signal sources to which thecoverage area maps correspond to utilize in positioning to therequesting apparatus, wherein the map type is at least one of a mediantype, a mode type or a mean type.
 6. The apparatus of claim 5, whereinthe at least one memory and the computer program code are furtherconfigured to, in cooperation with the at least one processor, cause theapparatus to transmit identities of particular signal sources to whichthe coverage area maps correspond to utilize in positioning to therequesting apparatus.